Conventional seismic data acquisition techniques involve the use of an appropriate signal source to generate seismic energy and a set of corresponding receivers, such as geophones, spaced along or near the surface, to detect any reflected seismic signals due to seismic energy striking subsurface geological boundaries. The seismic signals are generated sequentially at each of a number of points along a seismic prospecting path while reflections are recorded at all of the points following generation of each signal. The recorded signals are then organized into gathers of traces each corresponding to a common depth point or common midpoint along the prospect path. The basic purpose of this exploration technique is to allow the signals within each gather to be combined to improve the signal to noise ratio. Due to the different path lengths involved in each source and receiver pair, corrections must be made to the individual traces within the gather to place them in alignment before stacking. These corrections are performed by a processing technique referred to as hyperbolic normal moveout (NMO), the accuracy of which depends primarily on estimated velocities of the signals passing through the earth formations.
Accurate determination of the velocity distribution of the subsurface is necessary for obtaining accurate images of subsurface formations because errors in velocity estimation result in errors in the alignment of these signals and thereby reduce the signal-to-noise ratio of the resulting stacked signal. Traditional seismic processing techniques, such as ray map migration, and new developments, such as amplitude versus offset (AVO) and multiparameter inversion techniques, are critically dependent upon the low-frequency velocity field. Prestack reflection traveltimes of seismic signals are required input for any algorithm attempting to map accurately the velocity distribution of the subsurface. The nonhyperbolic nature of prestack traveltime curves even in the presence of flat bedding-plane geometry has long been recognized, and further, large model-dependent oscillations can be observed, especially at large offset-to-depth ratios. Compounding the problem, lateral velocity variations and data acquisition complications, such as streamer feathering, can introduce traveltime perturbations of arbitrary, non-hyperbolic shape. Although these problems are well known, for reasons of efficiency and signal-to-noise ratio considerations, stacking velocity analyses typically are still carried out as a two-parameter hyperbolic search.
A recently developed strategy for determining velocity variations in a stratified earth, commonly referred to as tomography, has been used to produce enhanced subsurface images. Traveltime tomography techniques involve three steps: identifying a number of key horizons in a stacked section; determining the corresponding prestacked traveltimes; and solving for a velocity-depth model that reproduces the observed traveltimes.
However, tomography is not routinely practiced because of its requirement for accurate prestack reflection traveltime selection. Three different schemes are usually employed for selecting, or "picking," traveltimes, each of which has certain disadvantages. One approach is to digitize prestacked traveltime data on an interactive work station. However, this is a very tedious process requiring the display and selection of thousands of seismic events. A second approach is to use stacking velocities and zero-offset times to construct a subsurface macro model. However, this technique is based essentially on a hyperbolic approximation to arrival times, and is therefore inaccurate. A third approach is to overcome the need for accurate traveltime picking by employing a coherency inversion method which does not depend on prestack time picking and is not based on curve fitting or hyperbolic approximations. The method is formulated as one of global optimization of some energy function. An optimization algorithm produces a velocity model which maximizes some measure of coherency. This measure is calculated on unstacked trace gathers in a time window along the traveltime curves which are determined by tracing rays through the model. Knowledge of zero-offset traveltimes for principal reflectors (for example, from post-stack picking) is used alternatively to update velocities using the coherency measure and the interface position using zero offset time information until an optimal solution is obtained. Since velocity updating using the coherency measure is a highly nonlinear process, it is performed using a type of Monte Carlo technique referred to as the generalized simulated annealing method for updating the velocity field. Coherency optimization by simulated annealing is described in detail in E. Landa et al., "Reference Velocity Model Estimation from Prestack Waveforms: Coherency Optimization by Simulated Annealing," 54 Geophysics 984-990 (1989). However, a major disadvantage of the foregoing coherency optimization method is that it requires large computational resources to perform computations for a large number of model parameters, with the ever present risk of inaccuracy due to cycle skipping and convergence to local minima.
What is needed is a tomographic technique for analyzing seismic data in which prestack traveltimes are determined by a method other than manual picking, since prestack traveltimes are a required input for any algorithm attempting to map accurately the velocity distribution of a subsurface. Such a technique would enable reference velocity model estimation based upon a traveltime-based solution as a preferred alternative to coherency optimization for the reasons described above.